Yolov5-PB: a traffic sign detection algorithm based on deep learning
Peirui Bai, Qiu Meng, Chuanxiang Ren, Yongjian Wan, Fangfang Fu · 2023
For the real road environment, the proportion of traffic sign targets is small and not easy to be detected, and the recognition background is complex and changeable, resulting in the problems of low detection precision and poor robustness. In this paper, a traffic sign detection algorithm based on improved Yolov5-PB is proposed. The algorithm uses the ParC-Net as the backbone feature extraction network to fully extract the target feature information. The BiFPN structure is adopted to enhance the feature fusion ability of the network for multi-scale traffic signs. Simulation is carried out on TT100K and GTSDB traffic sign datasets, and the results show that the [email protected] of Yolov5-PB algorithm reaches 85.6% and 72.2%, respectively, which are 3.1% and 19.2% higher than Yolov5. The precision reaches 85.3% and 71.6%, respectively, which are 0.3% and 21.8% higher than Yolov5. Furthermore, compared with the current mainstream target detection algorithms, the proposed algorithm can achieve better detection precision and meet the requirements of real-time detection, and has better robustness.